Spotify - preprocessing, descriptive statistics and bivariate statistics

Introduction

In this project, we are using a dataset of songs on the music streaming app Spotify.

The dataset contains songs on Spotify across multiple genres, and we will be performing several analyses to this dataset, such as basic descriptive and bivariate statistics, Principal Component Analysis, decision trees, regression, and clustering.

Here is the link to the original dataset

First, we import the data to R and make sure R is reading the data properly.

# importing relevant libraries to perform cleaning on the data
library(tidyverse)
library(janitor)
setwd("~/Documents/class/stats-final-project/")

# importing the data and cleaning the names into a snake_case format.
raw_data <- read.csv("dataset.csv") %>% clean_names() 

The dataset has 114,000 rows and 21 columns/variables.

It has the following scores (numerical variables): * popularity: The popularity of a track is a value between 0 and 100, with 100 being the most popular. - duration_ms: The track length in milliseconds - danceability: Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable - energy: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale - loudness: The overall loudness of a track in decibels (dB) - speechiness: Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. - acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic - instrumentalness: Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content - liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live - valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry) - tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration

The dataset also has the following categorical variables: - explicit: Whether or not the track has explicit lyrics (true = yes it does; false = no it does not OR unknown) - mode: Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0 - key: The key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1 - track_genre: The genre in which the track belongs

It also has the following columns that describe the songs: - track_id: The Spotify ID for the track - artists: The artists’ names who performed the track. If there is more than one artist, they are separated by a ; - album_name: The album name in which the track appears - track_name: Name of the track

Now we are performing several checks on the data

#dim(raw_data)
#names(raw_data)
#sapply(raw_data, class)

We’ll perform the following transforms to the data in order to prepare it for our analysis:

Since some of the categorical variables - mode, key, and time_signature - are currently codified as numerical, we will do the following: 1. key: We will be converting them from numbers 0 to 11 to the letter value of the key (C = 0, C# = 1, etc.) 2. mode: Instead of 0 for minor and 1 for major, we’ll convert them to “major” and “minor”. 3. time_signature: We’ll convert into characters instead of numeric.

We are also adding 2 more variables, which are: 1. multiple_artists: If there are multiple artists performing the track, the artists column will contain all artists separated by a semicolon (;). We’ll add a true value in this column if there are multiple artists, and false if a single artist. 2. tempo_cat: this is a categorical variable based on the tempo column. We’ll use the beats per minute to determine which tempo marking it fits in. This will be an ordinal variable, with the levels defined.

We are also performing several filters to scope our analysis: 1. Filter to songs that are done by popular artists. This is done by finding the artists that have 20 songs or more and filtering to just the songs by those artists. 2. We also scope the analysis to just songs that are less than 10 minutes. 3. We’ll also remove the duplicated songs. This is because some songs are listed in albums or single versions. This is done by removing songs that have the same variable in the track_name and artists columns. 4. We also sample the data to just 3,000 rows/songs. This is done by random sampling using the sample_n() function.

Finally, we’ll just select the columns that are relevant to us in our analysis, and remove the descriptive columns, track_id, artists, album_name, and track_name.

Descriptive Statistics

dd```

Error: attempt to use zero-length variable name

Bivariate statistics

After seeing the basic descriptive statistics of the data, we’ll do a bivariate statistics analysis. The purpose is to find relationships between: 1. Categorical vs categorical variables 2. Categorical vs numerical variables 3. Numerical vs numerical variables

Categorical vs. categorical

We examine the relationship between tempo marking and mode.

library(ggplot2)

# stacked bar chart

ggplot(dd, 
       aes(x = tempo_cat, 
           fill = mode)) + 
  geom_bar(position = "stack")

ggplot(dd, 
       aes(x = tempo_cat, 
           fill = mode)) + 
  geom_bar(position = "dodge")

ggplot(dd, 
       aes(x = tempo_cat, 
           fill = mode)) + 
  geom_bar(position = "fill")

From the above bar charts, we see that while there are more songs in the major key, some tempo markings have a higher proportion of minor songs than others. Larghetto and Vivace songs are the two tempo markings that have the highest proportion of minor songs, which is interesting because Larghetto is on the slower end, and Vivace is on the faster end.

Next, we can also examine the relationship between mode and explicitness.

# stacked bar chart

ggplot(dd, 
       aes(x = explicit, 
           fill = mode)) + 
  geom_bar(position = "stack")

ggplot(dd, 
       aes(x = explicit, 
           fill = mode)) + 
  geom_bar(position = "dodge")

ggplot(dd, 
       aes(x = explicit, 
           fill = mode)) + 
  geom_bar(position = "fill")

From the plots above: there aren’t many songs that are explicit, and it is hard to tell the relationship. The proportion of minor songs for songs that are explicit is slightly higher than major songs. However, it’s a very minimal difference.

We can also examine the relationship between track_genre and mode.

# stacked bar chart
ggplot(dd, 
       aes(x = track_genre, 
           fill = mode)) + 
  geom_bar(position = "stack") + scale_x_discrete(guide = guide_axis(angle = 90))

ggplot(dd, 
       aes(x = track_genre, 
           fill = mode)) + 
  geom_bar(position = "dodge") + scale_x_discrete(guide = guide_axis(angle = 90))

ggplot(dd, 
       aes(x = track_genre, 
           fill = mode)) + 
  geom_bar(position = "fill") + scale_x_discrete(guide = guide_axis(angle = 90)) +
    theme(legend.key.size = unit(0.5, 'cm'), #change legend key size
        legend.key.height = unit(0.5, 'cm'), #change legend key height
        legend.key.width = unit(0.5, 'cm'), #change legend key width
        legend.title = element_text(size=5), #change legend title font size
        legend.text = element_text(size=5)) +
  theme(text = element_text(size = 7)) 

# getting the results of the proportion bar chart in a table
dd %>% group_by(track_genre, mode) %>% 
  summarise(n = n()) %>% 
  mutate(freq = n / sum(n)) %>% 
  filter(mode == "minor") %>% 
  arrange(desc(freq))

`summarise()` has grouped output by 'track_genre'. You can override using the `.groups` argument.

NA

We can see there are some genres that clearly stand out that have majority minor songs. All latin songs are in the minor key. Synth-pop, turkish, trance, dancehall, romance, spanish, anime and hiphop songs are also among the top 10 genres with a high proportion of minor songs.

So, there seem to be a relationship between genre and mode.

# stacked bar chart
ggplot(dd, 
       aes(x = track_genre, 
           fill = explicit)) + 
  geom_bar(position = "stack") + scale_x_discrete(guide = guide_axis(angle = 90))

ggplot(dd, 
       aes(x = track_genre, 
           fill = explicit)) + 
  geom_bar(position = "dodge") + scale_x_discrete(guide = guide_axis(angle = 90))

ggplot(dd, 
       aes(x = track_genre, 
           fill = explicit)) + 
  geom_bar(position = "fill") + scale_x_discrete(guide = guide_axis(angle = 90)) +
    theme(legend.key.size = unit(0.5, 'cm'), #change legend key size
        legend.key.height = unit(0.5, 'cm'), #change legend key height
        legend.key.width = unit(0.5, 'cm'), #change legend key width
        legend.title = element_text(size=5), #change legend title font size
        legend.text = element_text(size=5)) +
  theme(text = element_text(size = 7)) 

# getting the results of the proportion bar chart in a table
dd %>% group_by(track_genre, explicit) %>% 
  summarise(n = n()) %>% 
  mutate(freq = n / sum(n)) %>% 
  filter(explicit == "True") %>% 
  arrange(desc(freq))

`summarise()` has grouped output by 'track_genre'. You can override using the `.groups` argument.

Explicit and genre also seems to be related, as songs that are explicit tend to be from some genres. Latino songs are 100% explicit. Comedy, country, dance and some of the metal genres also tend to contain swear words.

Next we can examine the relationship between key and time signature.

# stacked bar chart

ggplot(dd, 
       aes(x = key, 
           fill = mode)) + 
  geom_bar(position = "stack")

ggplot(dd, 
       aes(x = key, 
           fill = mode)) + 
  geom_bar(position = "dodge")

ggplot(dd, 
       aes(x = key, 
           fill = mode)) + 
  geom_bar(position = "fill")

# getting the results of the proportion bar chart in a table
dd %>% group_by(key, mode) %>% 
  summarise(n = n()) %>% 
  mutate(freq = n / sum(n)) %>% 
  filter(mode == "minor") %>% 
  arrange(desc(freq))

`summarise()` has grouped output by 'key'. You can override using the `.groups` argument.

The key of B has the highest proportion of minor songs. F#/Gb, E and A#/Bb also have a relatively higher percentage of minor songs.

Categorical vs numerical variables

We can also perform analysis on categorical vs numerical variables by using charts such as multiple boxplots to plot the distribution of one numerical variable given another categorical variable.

We have used the code by Dr. Karina Gibert to do an overview of the variables, and below are the interesting plots in ggplot()

First, the code below creates functions to test numerical and qualitative variables.

#Calcula els valor test de la variable Xnum per totes les modalitats del factor P
ValorTestXnum <- function(Xnum,P){
  #freq dis of fac
  nk <- as.vector(table(P)); 
  n <- sum(nk); 
  #mitjanes x grups
  xk <- tapply(Xnum,P,mean);
  #valors test
  txk <- (xk-mean(Xnum))/(sd(Xnum)*sqrt((n-nk)/(n*nk))); 
  #p-values
  pxk <- pt(txk,n-1,lower.tail=F);
  for(c in 1:length(levels(as.factor(P)))){if (pxk[c]>0.5){pxk[c]<-1-pxk[c]}}
  return (pxk)
}

ValorTestXquali <- function(P,Xquali){
  taula <- table(P,Xquali);
  n <- sum(taula); 
  pk <- apply(taula,1,sum)/n;
  pj <- apply(taula,2,sum)/n;
  pf <- taula/(n*pk);
  pjm <- matrix(data=pj,nrow=dim(pf)[1],ncol=dim(pf)[2], byrow=TRUE);      
  dpf <- pf - pjm; 
  dvt <- sqrt(((1-pk)/(n*pk))%*%t(pj*(1-pj))); 
  #i hi ha divisions iguals a 0 dona NA i no funciona
  zkj <- dpf
  zkj[dpf!=0]<-dpf[dpf!=0]/dvt[dpf!=0]; 
  pzkj <- pnorm(zkj,lower.tail=F);
  for(c in 1:length(levels(as.factor(P)))){for (s in 1:length(levels(Xquali))){if (pzkj[c,s]> 0.5){pzkj[c,s]<-1- pzkj[c,s]}}}
  return (list(rowpf=pf,vtest=zkj,pval=pzkj))
}

There are several variables we wish to assess. Our hypotheses are:

Let’s run the profiling script for the mode variable

#Data is referred to as "dades" in the following code

dades<-dd
K<-dim(dades)[2]
#par(ask=TRUE)

#P must contain the class variable
P<-dd$mode
nameP<-"mode"



nc<-length(levels(as.factor(P)))
pvalk <- matrix(data=0,nrow=nc,ncol=K, dimnames=list(levels(P),names(dades)))
nameP<-"mode"
n<-dim(dades)[1]

for(k in 1:K){
  if (is.numeric(dades[,k])){ 
    print(paste("Analysis by class of the Variable:", names(dades)[k]))
    
    boxplot(dades[,k]~P, main=paste("Boxplot of", names(dades)[k], "vs", nameP ), horizontal=TRUE)
    
    barplot(tapply(dades[[k]], P, mean),main=paste("Means of", names(dades)[k], "by", nameP ))
    abline(h=mean(dades[[k]]))
    legend(0,mean(dades[[k]]),"global mean",bty="n")
    print("Statistics by groups:")
    for(s in levels(as.factor(P))) {print(summary(dades[P==s,k]))}
    o<-oneway.test(dades[,k]~P)
    print(paste("p-valueANOVA:", o$p.value))
    kw<-kruskal.test(dades[,k]~P)
    print(paste("p-value Kruskal-Wallis:", kw$p.value))
    pvalk[,k]<-ValorTestXnum(dades[,k], P)
    print("p-values ValorsTest: ")
    print(pvalk[,k])      
  }else{
    if(class(dd[,k])=="Date"){
      print(summary(dd[,k]))
      print(sd(dd[,k]))
      #decide breaks: weeks, months, quarters...
      hist(dd[,k],breaks="weeks")
    }else{
      #qualitatives
      print(paste("Variable", names(dades)[k]))
      table<-table(P,dades[,k])
      #   print("Cross-table")
      #   print(table)
      rowperc<-prop.table(table,1)
      
      colperc<-prop.table(table,2)
      #  print("Distribucions condicionades a files")
      # print(rowperc)
      
      #ojo porque si la variable es true o false la identifica amb el tipus Logical i
      #aquest no te levels, por tanto, coertion preventiva
      
      dades[,k]<-as.factor(dades[,k])
      
      
      marg <- table(as.factor(P))/n
      print(append("Categories=",levels(as.factor(dades[,k]))))
      
      #from next plots, select one of them according to your practical case
      
      #with legend
      plot(marg,type="l",ylim=c(0,1),main=paste("Prop. of pos & neg by",names(dades)[k]))
      paleta<-rainbow(length(levels(dades[,k])))
      for(c in 1:length(levels(dades[,k]))){lines(colperc[,c],col=paleta[c]) }
      legend("topright", levels(dades[,k]), col=paleta, lty=2, cex=0.6)
      
      #condicionades a classes
      #with legend
      plot(marg,type="n",ylim=c(0,1),main=paste("Prop. of pos & neg by",names(dades)[k]))
      paleta<-rainbow(length(levels(dades[,k])))
      for(c in 1:length(levels(dades[,k]))){lines(rowperc[,c],col=paleta[c]) }
      legend("topright", levels(dades[,k]), col=paleta, lty=2, cex=0.6)
      
      #amb variable en eix d'abcisses
      marg <-table(dades[,k])/n
      print(append("Categories=",levels(dades[,k])))
      
      #with legend
      plot(marg,type="l",ylim=c(0,1),main=paste("Prop. of pos & neg by",names(dades)[k]), las=3)
      for(c in 1:length(levels(as.factor(P)))){lines(rowperc[c,],col=paleta[c])}
      legend("topright", levels(as.factor(P)), col=paleta, lty=2, cex=0.6)
      
      #condicionades a columna 
      #with legend
      plot(marg,type="n",ylim=c(0,1),main=paste("Prop. of pos & neg by",names(dades)[k]), las=3)
      for(c in 1:length(levels(as.factor(P)))){lines(colperc[c,],col=paleta[c])}
      legend("topright", levels(as.factor(P)), col=paleta, lty=2, cex=0.6)
      
      table<-table(dades[,k],P)
      print("Cross Table:")
      print(table)
      print("Distribucions condicionades a columnes:")
      print(colperc)
      
      #diagrames de barres apilades                                         
      
      paleta<-rainbow(length(levels(dades[,k])))
      
      barplot(table(dades[,k], as.factor(P)), beside=FALSE,col=paleta )
      legend("topright",levels(as.factor(dades[,k])),pch=1,cex=0.5, col=paleta)
      
      #diagrames de barres adosades
      
      barplot(table(dades[,k], as.factor(P)), beside=TRUE,col=paleta)
      legend("topright",levels(as.factor(dades[,k])),pch=1,cex=0.5, col=paleta)
      
      print("Test Chi quadrat: ")
      print(chisq.test(dades[,k], as.factor(P)))
      
      print("valorsTest:")
      print( ValorTestXquali(P,dades[,k]))
      #calcular els pvalues de les quali
    }
  }
}#endfor

[1] "Analysis by class of the Variable: popularity"

[1] "Statistics by groups:"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00   20.00   31.00   33.29   46.00   93.00 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00   17.00   29.00   31.73   46.00   97.00 
[1] "p-valueANOVA: 0.0400221586551545"
[1] "p-value Kruskal-Wallis: 0.0120068574469301"
[1] "p-values ValorsTest: "
[1] 0.01756552 0.01756552
[1] "Analysis by class of the Variable: duration_ms"

[1] "Statistics by groups:"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  28946  164319  213993  220668  265283  594533 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  30622  171994  221756  231206  277523  579546 
[1] "p-valueANOVA: 0.00239712152994276"
[1] "p-value Kruskal-Wallis: 0.00437920349247436"
[1] "p-values ValorsTest: "
[1] 0.0009370723 0.0009370723
[1] "Variable explicit"
[1] "Categories=" "False"       "True"       

[1] "Categories=" "False"       "True"       

[1] "Cross Table:"
       P
        major minor
  False  1899   913
  True    123    65
[1] "Distribucions condicionades a columnes:"
       
P           False      True
  major 0.6753201 0.6542553
  minor 0.3246799 0.3457447

[1] "Test Chi quadrat: "

    Pearson's Chi-squared test with Yates' continuity correction

data:  dades[, k] and as.factor(P)
X-squared = 0.26645, df = 1, p-value = 0.6057

[1] "valorsTest:"
$rowpf
       Xquali
P            False       True
  major 0.93916914 0.06083086
  minor 0.93353783 0.06646217

$vtest
       Xquali
P            False       True
  major  0.5965451 -0.5965451
  minor -0.5965451  0.5965451

$pval
       Xquali
P           False      True
  major 0.2754056 0.2754056
  minor 0.2754056 0.2754056

[1] "Analysis by class of the Variable: danceability"

[1] "Statistics by groups:"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.4290  0.5460  0.5412  0.6637  0.9750 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.4243  0.5570  0.5405  0.6690  0.9530 
[1] "p-valueANOVA: 0.919396530566418"
[1] "p-value Kruskal-Wallis: 0.542364091950772"
[1] "p-values ValorsTest: "
[1] 0.4592491 0.4592491
[1] "Analysis by class of the Variable: energy"

[1] "Statistics by groups:"
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000242 0.401250 0.635000 0.608990 0.851000 0.999000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00983 0.48900 0.71100 0.65809 0.87475 0.99900 
[1] "p-valueANOVA: 1.42880872470579e-06"
[1] "p-value Kruskal-Wallis: 3.11239888728683e-06"
[1] "p-values ValorsTest: "
[1] 1.116477e-06 1.116477e-06
[1] "Variable key"
 [1] "Categories=" "A"           "A#/Bb"       "B"           "C"           "C#/Db"       "D"          
 [8] "D#/Eb"       "E"           "F"           "F#/Gb"       "G"           "G#/Ab"      

 [1] "Categories=" "A"           "A#/Bb"       "B"           "C"           "C#/Db"       "D"          
 [8] "D#/Eb"       "E"           "F"           "F#/Gb"       "G"           "G#/Ab"      

[1] "Cross Table:"
       P
        major minor
  A       202   124
  A#/Bb   106    81
  B       102   119
  C       291    75
  C#/Db   170    80
  D       266    80
  D#/Eb    66    24
  E       149   120
  F       151   101
  F#/Gb    87    73
  G       315    68
  G#/Ab   117    33
[1] "Distribucions condicionades a columnes:"
       
P               A     A#/Bb         B         C     C#/Db         D     D#/Eb         E         F     F#/Gb
  major 0.6196319 0.5668449 0.4615385 0.7950820 0.6800000 0.7687861 0.7333333 0.5539033 0.5992063 0.5437500
  minor 0.3803681 0.4331551 0.5384615 0.2049180 0.3200000 0.2312139 0.2666667 0.4460967 0.4007937 0.4562500
       
P               G     G#/Ab
  major 0.8224543 0.7800000
  minor 0.1775457 0.2200000

[1] "Test Chi quadrat: "

    Pearson's Chi-squared test

data:  dades[, k] and as.factor(P)
X-squared = 182.12, df = 11, p-value < 2.2e-16

[1] "valorsTest:"
$rowpf
       Xquali
P                A      A#/Bb          B          C      C#/Db          D      D#/Eb          E          F
  major 0.09990109 0.05242334 0.05044510 0.14391691 0.08407517 0.13155292 0.03264095 0.07368942 0.07467854
  minor 0.12678937 0.08282209 0.12167689 0.07668712 0.08179959 0.08179959 0.02453988 0.12269939 0.10327198
       Xquali
P            F#/Gb          G      G#/Ab
  major 0.04302671 0.15578635 0.05786350
  minor 0.07464213 0.06952965 0.03374233

$vtest
       Xquali
P                A      A#/Bb          B          C      C#/Db          D      D#/Eb          E          F
  major -2.2181664 -3.2282754 -7.0009031  5.2739257  0.2113863  3.9990262  1.2192574 -4.4042106 -2.6465403
  minor  2.2181664  3.2282754  7.0009031 -5.2739257 -0.2113863 -3.9990262 -1.2192574  4.4042106  2.6465403
       Xquali
P            F#/Gb          G      G#/Ab
  major -3.6124383  6.6360891  2.8415215
  minor  3.6124383 -6.6360891 -2.8415215

$pval
       Xquali
P                  A        A#/Bb            B            C        C#/Db            D        D#/Eb
  major 1.327175e-02 6.226950e-04 1.271538e-12 6.676800e-08 4.162929e-01 3.180182e-05 1.113733e-01
  minor 1.327175e-02 6.226950e-04 1.271589e-12 6.676800e-08 4.162929e-01 3.180182e-05 1.113733e-01
       Xquali
P                  E            F        F#/Gb            G        G#/Ab
  major 5.308488e-06 4.065990e-03 1.516657e-04 1.610576e-11 2.244941e-03
  minor 5.308488e-06 4.065990e-03 1.516657e-04 1.610578e-11 2.244941e-03

[1] "Analysis by class of the Variable: loudness"

[1] "Statistics by groups:"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-37.417 -11.354  -7.519  -8.891  -5.186   0.377 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-42.631 -10.707  -7.438  -8.883  -5.177  -1.134 
[1] "p-valueANOVA: 0.971118589129941"
[1] "p-value Kruskal-Wallis: 0.592448123464342"
[1] "p-values ValorsTest: "
[1] 0.4853843 0.4853843
[1] "Variable mode"
[1] "Categories=" "major"       "minor"      

[1] "Categories=" "major"       "minor"      

[1] "Cross Table:"
       P
        major minor
  major  2022     0
  minor     0   978
[1] "Distribucions condicionades a columnes:"
       
P       major minor
  major     1     0
  minor     0     1

[1] "Test Chi quadrat: "

    Pearson's Chi-squared test with Yates' continuity correction

data:  dades[, k] and as.factor(P)
X-squared = 2995.5, df = 1, p-value < 2.2e-16

[1] "valorsTest:"
$rowpf
       Xquali
P       major minor
  major     1     0
  minor     0     1

$vtest
       Xquali
P           major     minor
  major  54.77226 -54.77226
  minor -54.77226  54.77226

$pval
       Xquali
P       major minor
  major     0     0
  minor     0     0

[1] "Analysis by class of the Variable: speechiness"

[1] "Statistics by groups:"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.03380 0.04490 0.07438 0.07130 0.96200 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.03712 0.05025 0.08738 0.08165 0.95500 
[1] "p-valueANOVA: 0.00233148377089234"
[1] "p-value Kruskal-Wallis: 6.65362573251419e-08"
[1] "p-values ValorsTest: "
[1] 0.0006716785 0.0006716785
[1] "Analysis by class of the Variable: acousticness"

[1] "Statistics by groups:"
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000001 0.018950 0.270000 0.368455 0.703750 0.996000 
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000011 0.0055125 0.1380000 0.3071756 0.6167500 0.9960000 
[1] "p-valueANOVA: 7.06558400528732e-06"
[1] "p-value Kruskal-Wallis: 1.31911871199142e-06"
[1] "p-values ValorsTest: "
[1] 3.584032e-06 3.584032e-06
[1] "Analysis by class of the Variable: instrumentalness"

[1] "Statistics by groups:"
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0000000 0.0000328 0.1591862 0.0473750 1.0000000 
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0000000 0.0008475 0.2497380 0.6480000 1.0000000 
[1] "p-valueANOVA: 9.57965588130597e-11"
[1] "p-value Kruskal-Wallis: 4.72350468857676e-13"
[1] "p-values ValorsTest: "
[1] 3.990808e-12 3.990828e-12
[1] "Analysis by class of the Variable: liveness"

[1] "Statistics by groups:"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0204  0.1020  0.1425  0.2395  0.3068  0.9920 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0112  0.0991  0.1385  0.2346  0.3068  0.9890 
[1] "p-valueANOVA: 0.557457582036754"
[1] "p-value Kruskal-Wallis: 0.4249753015773"
[1] "p-values ValorsTest: "
[1] 0.2796026 0.2796026
[1] "Analysis by class of the Variable: valence"

[1] "Statistics by groups:"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.2590  0.4880  0.4860  0.7007  0.9830 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.2190  0.4470  0.4588  0.6797  0.9850 
[1] "p-valueANOVA: 0.00926221933990095"
[1] "p-value Kruskal-Wallis: 0.0079740097954662"
[1] "p-values ValorsTest: "
[1] 0.004419935 0.004419935
[1] "Analysis by class of the Variable: tempo"

[1] "Statistics by groups:"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    0.0   100.7   122.4   122.9   140.0   209.1 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00   98.19  120.12  119.87  137.69  219.97 
[1] "p-valueANOVA: 0.00884088440664359"
[1] "p-value Kruskal-Wallis: 0.00992439951138771"
[1] "p-values ValorsTest: "
[1] 0.004591509 0.004591509
[1] "Variable time_signature"
[1] "Categories=" "0"           "1"           "3"           "4"           "5"          

[1] "Categories=" "0"           "1"           "3"           "4"           "5"          

[1] "Cross Table:"
   P
    major minor
  0     2     2
  1    21     9
  3   187    84
  4  1772   864
  5    40    19
[1] "Distribucions condicionades a columnes:"
       
P               0         1         3         4         5
  major 0.5000000 0.7000000 0.6900369 0.6722307 0.6779661
  minor 0.5000000 0.3000000 0.3099631 0.3277693 0.3220339

[1] "Test Chi quadrat: "

Warning: Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  dades[, k] and as.factor(P)
X-squared = 1.0024, df = 4, p-value = 0.9094

[1] "valorsTest:"
$rowpf
       Xquali
P                  0            1            3            4            5
  major 0.0009891197 0.0103857567 0.0924826904 0.8763600396 0.0197823937
  minor 0.0020449898 0.0092024540 0.0858895706 0.8834355828 0.0194274029

$vtest
       Xquali
P                 0           1           3           4           5
  major -0.74289976  0.30533573  0.59050719 -0.55636144  0.06563934
  minor  0.74289976 -0.30533573 -0.59050719  0.55636144 -0.06563934

$pval
       Xquali
P               0         1         3         4         5
  major 0.2287712 0.3800552 0.2774253 0.2889819 0.4738325
  minor 0.2287712 0.3800552 0.2774253 0.2889819 0.4738325

[1] "Variable track_genre"
  [1] "Categories="       "acoustic"          "afrobeat"          "alt-rock"          "alternative"      
  [6] "ambient"           "anime"             "black-metal"       "bluegrass"         "blues"            
 [11] "brazil"            "breakbeat"         "british"           "cantopop"          "chicago-house"    
 [16] "children"          "chill"             "classical"         "club"              "comedy"           
 [21] "country"           "dance"             "dancehall"         "death-metal"       "detroit-techno"   
 [26] "disco"             "disney"            "drum-and-bass"     "edm"               "electro"          
 [31] "electronic"        "emo"               "folk"              "forro"             "garage"           
 [36] "german"            "gospel"            "goth"              "grindcore"         "groove"           
 [41] "grunge"            "guitar"            "happy"             "hard-rock"         "hardcore"         
 [46] "heavy-metal"       "hip-hop"           "honky-tonk"        "idm"               "indian"           
 [51] "indie"             "indie-pop"         "industrial"        "iranian"           "j-dance"          
 [56] "j-idol"            "j-pop"             "j-rock"            "jazz"              "k-pop"            
 [61] "kids"              "latin"             "latino"            "malay"             "mandopop"         
 [66] "metal"             "metalcore"         "minimal-techno"    "mpb"               "new-age"          
 [71] "opera"             "pagode"            "party"             "piano"             "pop"              
 [76] "pop-film"          "power-pop"         "progressive-house" "psych-rock"        "punk"             
 [81] "punk-rock"         "r-n-b"             "rock-n-roll"       "rockabilly"        "romance"          
 [86] "salsa"             "samba"             "sertanejo"         "show-tunes"        "singer-songwriter"
 [91] "ska"               "sleep"             "spanish"           "study"             "swedish"          
 [96] "synth-pop"         "tango"             "trance"            "trip-hop"          "turkish"          
[101] "world-music"      

  [1] "Categories="       "acoustic"          "afrobeat"          "alt-rock"          "alternative"      
  [6] "ambient"           "anime"             "black-metal"       "bluegrass"         "blues"            
 [11] "brazil"            "breakbeat"         "british"           "cantopop"          "chicago-house"    
 [16] "children"          "chill"             "classical"         "club"              "comedy"           
 [21] "country"           "dance"             "dancehall"         "death-metal"       "detroit-techno"   
 [26] "disco"             "disney"            "drum-and-bass"     "edm"               "electro"          
 [31] "electronic"        "emo"               "folk"              "forro"             "garage"           
 [36] "german"            "gospel"            "goth"              "grindcore"         "groove"           
 [41] "grunge"            "guitar"            "happy"             "hard-rock"         "hardcore"         
 [46] "heavy-metal"       "hip-hop"           "honky-tonk"        "idm"               "indian"           
 [51] "indie"             "indie-pop"         "industrial"        "iranian"           "j-dance"          
 [56] "j-idol"            "j-pop"             "j-rock"            "jazz"              "k-pop"            
 [61] "kids"              "latin"             "latino"            "malay"             "mandopop"         
 [66] "metal"             "metalcore"         "minimal-techno"    "mpb"               "new-age"          
 [71] "opera"             "pagode"            "party"             "piano"             "pop"              
 [76] "pop-film"          "power-pop"         "progressive-house" "psych-rock"        "punk"             
 [81] "punk-rock"         "r-n-b"             "rock-n-roll"       "rockabilly"        "romance"          
 [86] "salsa"             "samba"             "sertanejo"         "show-tunes"        "singer-songwriter"
 [91] "ska"               "sleep"             "spanish"           "study"             "swedish"          
 [96] "synth-pop"         "tango"             "trance"            "trip-hop"          "turkish"          
[101] "world-music"      

[1] "Cross Table:"
                   P
                    major minor
  acoustic             19     4
  afrobeat             25    16
  alt-rock             27    19
  alternative           4     4
  ambient              35     6
  anime                12    20
  black-metal           8     9
  bluegrass            33     8
  blues                 2     0
  brazil               15     4
  breakbeat            22    14
  british              51    20
  cantopop             41    10
  chicago-house        32    35
  children             84    14
  chill                 6     0
  classical            18     4
  club                 21     3
  comedy               21    11
  country               5     0
  dance                 2     0
  dancehall             5     9
  death-metal          12     9
  detroit-techno       44    35
  disco                 3     1
  disney               33    10
  drum-and-bass         2     2
  edm                   2     1
  electro               8     3
  electronic            9    10
  emo                  20    10
  folk                 11     7
  forro                37    13
  garage               25     8
  german               14    10
  gospel               12     2
  goth                 32    15
  grindcore            24    25
  groove               26     8
  grunge               28    16
  guitar               21    10
  happy                19    11
  hard-rock            33    12
  hardcore              6     6
  heavy-metal          63    40
  hip-hop               3     5
  honky-tonk          106     3
  idm                  30    31
  indian                8     1
  indie                 1     0
  indie-pop             2     0
  industrial           30    17
  iranian              24    20
  j-dance              14    12
  j-idol               79    31
  j-pop                10     2
  j-rock               11     5
  jazz                  1     1
  k-pop                19    18
  kids                 64    20
  latin                 0     1
  latino                1     2
  malay                22    14
  mandopop             21     2
  metal                10     4
  metalcore            13    17
  minimal-techno        5     1
  mpb                  10     6
  new-age              47    16
  opera                14     6
  pagode               39    20
  party                15     5
  piano                22     6
  pop                   1     0
  pop-film              2     1
  power-pop            24     4
  progressive-house     2     2
  psych-rock           26    11
  punk                  4     1
  punk-rock            13     4
  r-n-b                11     0
  rock-n-roll          28     7
  rockabilly           22     7
  romance              19    34
  salsa                13     8
  samba                20     4
  sertanejo            36     8
  show-tunes           16     2
  singer-songwriter     8     0
  ska                  27    13
  sleep                16    18
  spanish               4     7
  study                35    39
  swedish               5     2
  synth-pop             5    13
  tango                25    20
  trance                3     7
  trip-hop             11    16
  turkish               2     5
  world-music          51     5
[1] "Distribucions condicionades a columnes:"
       
P         acoustic   afrobeat   alt-rock alternative    ambient      anime black-metal  bluegrass      blues
  major 0.82608696 0.60975610 0.58695652  0.50000000 0.85365854 0.37500000  0.47058824 0.80487805 1.00000000
  minor 0.17391304 0.39024390 0.41304348  0.50000000 0.14634146 0.62500000  0.52941176 0.19512195 0.00000000
       
P           brazil  breakbeat    british   cantopop chicago-house   children      chill  classical       club
  major 0.78947368 0.61111111 0.71830986 0.80392157    0.47761194 0.85714286 1.00000000 0.81818182 0.87500000
  minor 0.21052632 0.38888889 0.28169014 0.19607843    0.52238806 0.14285714 0.00000000 0.18181818 0.12500000
       
P           comedy    country      dance  dancehall death-metal detroit-techno      disco     disney
  major 0.65625000 1.00000000 1.00000000 0.35714286  0.57142857     0.55696203 0.75000000 0.76744186
  minor 0.34375000 0.00000000 0.00000000 0.64285714  0.42857143     0.44303797 0.25000000 0.23255814
       
P       drum-and-bass        edm    electro electronic        emo       folk      forro     garage     german
  major    0.50000000 0.66666667 0.72727273 0.47368421 0.66666667 0.61111111 0.74000000 0.75757576 0.58333333
  minor    0.50000000 0.33333333 0.27272727 0.52631579 0.33333333 0.38888889 0.26000000 0.24242424 0.41666667
       
P           gospel       goth  grindcore     groove     grunge     guitar      happy  hard-rock   hardcore
  major 0.85714286 0.68085106 0.48979592 0.76470588 0.63636364 0.67741935 0.63333333 0.73333333 0.50000000
  minor 0.14285714 0.31914894 0.51020408 0.23529412 0.36363636 0.32258065 0.36666667 0.26666667 0.50000000
       
P       heavy-metal    hip-hop honky-tonk        idm     indian      indie  indie-pop industrial    iranian
  major  0.61165049 0.37500000 0.97247706 0.49180328 0.88888889 1.00000000 1.00000000 0.63829787 0.54545455
  minor  0.38834951 0.62500000 0.02752294 0.50819672 0.11111111 0.00000000 0.00000000 0.36170213 0.45454545
       
P          j-dance     j-idol      j-pop     j-rock       jazz      k-pop       kids      latin     latino
  major 0.53846154 0.71818182 0.83333333 0.68750000 0.50000000 0.51351351 0.76190476 0.00000000 0.33333333
  minor 0.46153846 0.28181818 0.16666667 0.31250000 0.50000000 0.48648649 0.23809524 1.00000000 0.66666667
       
P            malay   mandopop      metal  metalcore minimal-techno        mpb    new-age      opera     pagode
  major 0.61111111 0.91304348 0.71428571 0.43333333     0.83333333 0.62500000 0.74603175 0.70000000 0.66101695
  minor 0.38888889 0.08695652 0.28571429 0.56666667     0.16666667 0.37500000 0.25396825 0.30000000 0.33898305
       
P            party      piano        pop   pop-film  power-pop progressive-house psych-rock       punk
  major 0.75000000 0.78571429 1.00000000 0.66666667 0.85714286        0.50000000 0.70270270 0.80000000
  minor 0.25000000 0.21428571 0.00000000 0.33333333 0.14285714        0.50000000 0.29729730 0.20000000
       
P        punk-rock      r-n-b rock-n-roll rockabilly    romance      salsa      samba  sertanejo show-tunes
  major 0.76470588 1.00000000  0.80000000 0.75862069 0.35849057 0.61904762 0.83333333 0.81818182 0.88888889
  minor 0.23529412 0.00000000  0.20000000 0.24137931 0.64150943 0.38095238 0.16666667 0.18181818 0.11111111
       
P       singer-songwriter        ska      sleep    spanish      study    swedish  synth-pop      tango
  major        1.00000000 0.67500000 0.47058824 0.36363636 0.47297297 0.71428571 0.27777778 0.55555556
  minor        0.00000000 0.32500000 0.52941176 0.63636364 0.52702703 0.28571429 0.72222222 0.44444444
       
P           trance   trip-hop    turkish world-music
  major 0.30000000 0.40740741 0.28571429  0.91071429
  minor 0.70000000 0.59259259 0.71428571  0.08928571

[1] "Test Chi quadrat: "

Warning: Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  dades[, k] and as.factor(P)
X-squared = 342.97, df = 99, p-value < 2.2e-16

[1] "valorsTest:"
$rowpf
       Xquali
P           acoustic     afrobeat     alt-rock  alternative      ambient        anime  black-metal
  major 0.0093966370 0.0123639960 0.0133531157 0.0019782394 0.0173095945 0.0059347181 0.0039564787
  minor 0.0040899796 0.0163599182 0.0194274029 0.0040899796 0.0061349693 0.0204498978 0.0092024540
       Xquali
P          bluegrass        blues       brazil    breakbeat      british     cantopop chicago-house
  major 0.0163204748 0.0009891197 0.0074183976 0.0108803165 0.0252225519 0.0202769535  0.0158259149
  minor 0.0081799591 0.0000000000 0.0040899796 0.0143149284 0.0204498978 0.0102249489  0.0357873211
       Xquali
P           children        chill    classical         club       comedy      country        dance
  major 0.0415430267 0.0029673591 0.0089020772 0.0103857567 0.0103857567 0.0024727992 0.0009891197
  minor 0.0143149284 0.0000000000 0.0040899796 0.0030674847 0.0112474438 0.0000000000 0.0000000000
       Xquali
P          dancehall  death-metal detroit-techno        disco       disney drum-and-bass          edm
  major 0.0024727992 0.0059347181   0.0217606330 0.0014836795 0.0163204748  0.0009891197 0.0009891197
  minor 0.0092024540 0.0092024540   0.0357873211 0.0010224949 0.0102249489  0.0020449898 0.0010224949
       Xquali
P            electro   electronic          emo         folk        forro       garage       german
  major 0.0039564787 0.0044510386 0.0098911968 0.0054401583 0.0182987141 0.0123639960 0.0069238378
  minor 0.0030674847 0.0102249489 0.0102249489 0.0071574642 0.0132924335 0.0081799591 0.0102249489
       Xquali
P             gospel         goth    grindcore       groove       grunge       guitar        happy
  major 0.0059347181 0.0158259149 0.0118694362 0.0128585559 0.0138476756 0.0103857567 0.0093966370
  minor 0.0020449898 0.0153374233 0.0255623722 0.0081799591 0.0163599182 0.0102249489 0.0112474438
       Xquali
P          hard-rock     hardcore  heavy-metal      hip-hop   honky-tonk          idm       indian
  major 0.0163204748 0.0029673591 0.0311572700 0.0014836795 0.0524233432 0.0148367953 0.0039564787
  minor 0.0122699387 0.0061349693 0.0408997955 0.0051124744 0.0030674847 0.0316973415 0.0010224949
       Xquali
P              indie    indie-pop   industrial      iranian      j-dance       j-idol        j-pop
  major 0.0004945598 0.0009891197 0.0148367953 0.0118694362 0.0069238378 0.0390702275 0.0049455984
  minor 0.0000000000 0.0000000000 0.0173824131 0.0204498978 0.0122699387 0.0316973415 0.0020449898
       Xquali
P             j-rock         jazz        k-pop         kids        latin       latino        malay
  major 0.0054401583 0.0004945598 0.0093966370 0.0316518299 0.0000000000 0.0004945598 0.0108803165
  minor 0.0051124744 0.0010224949 0.0184049080 0.0204498978 0.0010224949 0.0020449898 0.0143149284
       Xquali
P           mandopop        metal    metalcore minimal-techno          mpb      new-age        opera
  major 0.0103857567 0.0049455984 0.0064292779   0.0024727992 0.0049455984 0.0232443126 0.0069238378
  minor 0.0020449898 0.0040899796 0.0173824131   0.0010224949 0.0061349693 0.0163599182 0.0061349693
       Xquali
P             pagode        party        piano          pop     pop-film    power-pop progressive-house
  major 0.0192878338 0.0074183976 0.0108803165 0.0004945598 0.0009891197 0.0118694362      0.0009891197
  minor 0.0204498978 0.0051124744 0.0061349693 0.0000000000 0.0010224949 0.0040899796      0.0020449898
       Xquali
P         psych-rock         punk    punk-rock        r-n-b  rock-n-roll   rockabilly      romance
  major 0.0128585559 0.0019782394 0.0064292779 0.0054401583 0.0138476756 0.0108803165 0.0093966370
  minor 0.0112474438 0.0010224949 0.0040899796 0.0000000000 0.0071574642 0.0071574642 0.0347648262
       Xquali
P              salsa        samba    sertanejo   show-tunes singer-songwriter          ska        sleep
  major 0.0064292779 0.0098911968 0.0178041543 0.0079129575      0.0039564787 0.0133531157 0.0079129575
  minor 0.0081799591 0.0040899796 0.0081799591 0.0020449898      0.0000000000 0.0132924335 0.0184049080
       Xquali
P            spanish        study      swedish    synth-pop        tango       trance     trip-hop
  major 0.0019782394 0.0173095945 0.0024727992 0.0024727992 0.0123639960 0.0014836795 0.0054401583
  minor 0.0071574642 0.0398773006 0.0020449898 0.0132924335 0.0204498978 0.0071574642 0.0163599182
       Xquali
P            turkish  world-music
  major 0.0009891197 0.0252225519
  minor 0.0051124744 0.0051124744

$vtest
       Xquali
P          acoustic    afrobeat    alt-rock alternative     ambient       anime black-metal   bluegrass
  major  1.56202633 -0.88363567 -1.26920504 -1.05132097  2.47109352 -3.62773962 -1.79430299  1.80014769
  minor -1.56202633  0.88363567  1.26920504  1.05132097 -2.47109352  3.62773962  1.79430299 -1.80014769
       Xquali
P             blues      brazil   breakbeat     british    cantopop chicago-house    children       chill
  major  0.98387214  1.07721084 -0.80985627  0.80610523  1.99641506   -3.46831336  3.93256761  1.70525451
  minor -0.98387214 -1.07721084  0.80985627 -0.80610523 -1.99641506    3.46831336 -3.93256761 -1.70525451
       Xquali
P         classical        club      comedy     country       dance   dancehall death-metal detroit-techno
  major  1.44804271  2.10914819 -0.21535912  1.55641737  0.98387214 -2.53515488 -1.00628889    -2.24903619
  minor -1.44804271 -2.10914819  0.21535912 -1.55641737 -0.98387214  2.53515488  1.00628889     2.24903619
       Xquali
P             disco      disney drum-and-bass         edm     electro  electronic         emo        folk
  major  0.32448495  1.31665478   -0.74289976 -0.02711069  0.37762453 -1.86867112 -0.08612033 -0.57092391
  minor -0.32448495 -1.31665478    0.74289976  0.02711069 -0.37762453  1.86867112  0.08612033  0.57092391
       Xquali
P             forro      garage      german      gospel        goth   grindcore      groove      grunge
  major  1.00401409  1.02991266 -0.95139023  1.46531495  0.10099435 -2.77354083  1.13477881 -0.53654192
  minor -1.00401409 -1.02991266  0.95139023 -1.46531495 -0.10099435  2.77354083 -1.13477881  0.53654192
       Xquali
P            guitar       happy   hard-rock    hardcore heavy-metal     hip-hop  honky-tonk         idm
  major  0.04082647 -0.47757640  0.85555541 -1.28846152 -1.37372348 -1.80658028  6.77207948 -3.06709735
  minor -0.04082647  0.47757640 -0.85555541  1.28846152  1.37372348  1.80658028 -6.77207948  3.06709735
       Xquali
P            indian       indie   indie-pop  industrial     iranian     j-dance      j-idol       j-pop
  major  1.37736451  0.69558666  0.98387214 -0.52629977 -1.83253689 -1.48081438  1.00719343  1.17985557
  minor -1.37736451 -0.69558666 -0.98387214  0.52629977  1.83253689  1.48081438 -1.00719343 -1.17985557
       Xquali
P            j-rock        jazz       k-pop        kids       latin      latino       malay    mandopop
  major  0.11550911 -0.52513421 -2.09553725  1.74333225 -1.43811476 -1.25941475 -0.80985627  2.45512314
  minor -0.11550911  0.52513421  2.09553725 -1.74333225  1.43811476  1.25941475  0.80985627 -2.45512314
       Xquali
P             metal   metalcore minimal-techno         mpb     new-age       opera      pagode       party
  major  0.32232357 -2.82631279     0.83344750 -0.41925528  1.23271909  0.24888693 -0.21487067  0.72751565
  minor -0.32232357  2.82631279    -0.83344750  0.41925528 -1.23271909 -0.24888693  0.21487067 -0.72751565
       Xquali
P             piano         pop    pop-film   power-pop progressive-house  psych-rock        punk   punk-rock
  major  1.26702506  0.69558666 -0.02711069  2.07714339       -0.74289976  0.37478285  0.60156009  0.80012007
  minor -1.26702506 -0.69558666  0.02711069 -2.07714339        0.74289976 -0.37478285 -0.60156009 -0.80012007
       Xquali
P             r-n-b rock-n-roll  rockabilly     romance       salsa       samba   sertanejo  show-tunes
  major  2.31085590  1.59960995  0.97689101 -4.94404136 -0.53911670  1.67192841  2.05544803  1.95082482
  minor -2.31085590 -1.59960995 -0.97689101  4.94404136  0.53911670 -1.67192841 -2.05544803 -1.95082482
       Xquali
P       singer-songwriter         ska       sleep     spanish       study     swedish   synth-pop       tango
  major        1.96971630  0.01358332 -2.54478931 -2.20001730 -3.73555408  0.22765050 -3.59702239 -1.70790649
  minor       -1.96971630 -0.01358332  2.54478931  2.20001730  3.73555408 -0.22765050  3.59702239  1.70790649
       Xquali
P            trance    trip-hop     turkish world-music
  major -2.52730634 -2.96861847 -2.19416333  3.81479723
  minor  2.52730634  2.96861847  2.19416333 -3.81479723

$pval
       Xquali
P           acoustic     afrobeat     alt-rock  alternative      ambient        anime  black-metal
  major 5.914089e-02 1.884465e-01 1.021840e-01 1.465556e-01 6.735029e-03 1.429567e-04 3.638241e-02
  minor 5.914089e-02 1.884465e-01 1.021840e-01 1.465556e-01 6.735029e-03 1.429567e-04 3.638241e-02
       Xquali
P          bluegrass        blues       brazil    breakbeat      british     cantopop chicago-house
  major 3.591866e-02 1.625892e-01 1.406930e-01 2.090114e-01 2.100911e-01 2.294438e-02  2.618681e-04
  minor 3.591866e-02 1.625892e-01 1.406930e-01 2.090114e-01 2.100911e-01 2.294438e-02  2.618681e-04
       Xquali
P           children        chill    classical         club       comedy      country        dance
  major 4.202167e-05 4.407348e-02 7.380255e-02 1.746590e-02 4.147437e-01 5.980444e-02 1.625892e-01
  minor 4.202167e-05 4.407348e-02 7.380255e-02 1.746590e-02 4.147437e-01 5.980444e-02 1.625892e-01
       Xquali
P          dancehall  death-metal detroit-techno        disco       disney drum-and-bass          edm
  major 5.619881e-03 1.571383e-01   1.225510e-02 3.727855e-01 9.397718e-02  2.287712e-01 4.891857e-01
  minor 5.619881e-03 1.571383e-01   1.225510e-02 3.727855e-01 9.397718e-02  2.287712e-01 4.891857e-01
       Xquali
P            electro   electronic          emo         folk        forro       garage       german
  major 3.528548e-01 3.083429e-02 4.656854e-01 2.840256e-01 1.576859e-01 1.515255e-01 1.707032e-01
  minor 3.528548e-01 3.083429e-02 4.656854e-01 2.840256e-01 1.576859e-01 1.515255e-01 1.707032e-01
       Xquali
P             gospel         goth    grindcore       groove       grunge       guitar        happy
  major 7.141750e-02 4.597775e-01 2.772494e-03 1.282340e-01 2.957920e-01 4.837171e-01 3.164759e-01
  minor 7.141750e-02 4.597775e-01 2.772494e-03 1.282340e-01 2.957920e-01 4.837171e-01 3.164759e-01
       Xquali
P          hard-rock     hardcore  heavy-metal      hip-hop   honky-tonk          idm       indian
  major 1.961219e-01 9.879268e-02 8.476377e-02 3.541387e-02 6.347218e-12 1.080742e-03 8.419979e-02
  minor 1.961219e-01 9.879268e-02 8.476377e-02 3.541387e-02 6.347256e-12 1.080742e-03 8.419979e-02
       Xquali
P              indie    indie-pop   industrial      iranian      j-dance       j-idol        j-pop
  major 2.433439e-01 1.625892e-01 2.993400e-01 3.343574e-02 6.932802e-02 1.569209e-01 1.190288e-01
  minor 2.433439e-01 1.625892e-01 2.993400e-01 3.343574e-02 6.932802e-02 1.569209e-01 1.190288e-01
       Xquali
P             j-rock         jazz        k-pop         kids        latin       latino        malay
  major 4.540208e-01 2.997449e-01 1.806163e-02 4.063780e-02 7.520075e-02 1.039403e-01 2.090114e-01
  minor 4.540208e-01 2.997449e-01 1.806163e-02 4.063780e-02 7.520075e-02 1.039403e-01 2.090114e-01
       Xquali
P           mandopop        metal    metalcore minimal-techno          mpb      new-age        opera
  major 7.041817e-03 3.736038e-01 2.354363e-03   2.022962e-01 3.375148e-01 1.088403e-01 4.017241e-01
  minor 7.041817e-03 3.736038e-01 2.354363e-03   2.022962e-01 3.375148e-01 1.088403e-01 4.017241e-01
       Xquali
P             pagode        party        piano          pop     pop-film    power-pop progressive-house
  major 4.149341e-01 2.334551e-01 1.025732e-01 2.433439e-01 4.891857e-01 1.889416e-02      2.287712e-01
  minor 4.149341e-01 2.334551e-01 1.025732e-01 2.433439e-01 4.891857e-01 1.889416e-02      2.287712e-01
       Xquali
P         psych-rock         punk    punk-rock        r-n-b  rock-n-roll   rockabilly      romance
  major 3.539110e-01 2.737335e-01 2.118206e-01 1.042041e-02 5.484257e-02 1.643116e-01 3.825973e-07
  minor 3.539110e-01 2.737335e-01 2.118206e-01 1.042041e-02 5.484257e-02 1.643116e-01 3.825973e-07
       Xquali
P              salsa        samba    sertanejo   show-tunes singer-songwriter          ska        sleep
  major 2.949032e-01 4.726922e-02 1.991788e-02 2.553894e-02      2.443545e-02 4.945812e-01 5.467185e-03
  minor 2.949032e-01 4.726922e-02 1.991788e-02 2.553894e-02      2.443545e-02 4.945812e-01 5.467185e-03
       Xquali
P            spanish        study      swedish    synth-pop        tango       trance     trip-hop
  major 1.390283e-02 9.365116e-05 4.099590e-01 1.609404e-04 4.382685e-02 5.747060e-03 1.495709e-03
  minor 1.390283e-02 9.365116e-05 4.099590e-01 1.609404e-04 4.382685e-02 5.747060e-03 1.495709e-03
       Xquali
P            turkish  world-music
  major 1.411183e-02 6.814740e-05
  minor 1.411183e-02 6.814740e-05

[1] "Variable multiple_artists"
[1] "Categories=" "FALSE"       "TRUE"       

[1] "Categories=" "FALSE"       "TRUE"       

[1] "Cross Table:"
       P
        major minor
  FALSE  1965   962
  TRUE     57    16
[1] "Distribucions condicionades a columnes:"
       
P           FALSE      TRUE
  major 0.6713358 0.7808219
  minor 0.3286642 0.2191781

[1] "Test Chi quadrat: "

    Pearson's Chi-squared test with Yates' continuity correction

data:  dades[, k] and as.factor(P)
X-squared = 3.4033, df = 1, p-value = 0.06506

[1] "valorsTest:"
$rowpf
       Xquali
P            FALSE       TRUE
  major 0.97181009 0.02818991
  minor 0.98364008 0.01635992

$vtest
       Xquali
P           FALSE      TRUE
  major -1.971207  1.971207
  minor  1.971207 -1.971207

$pval
       Xquali
P            FALSE       TRUE
  major 0.02435008 0.02435008
  minor 0.02435008 0.02435008

[1] "Variable tempo_cat"
 [1] "Categories=" "Larghissimo" "Grave"       "Lento/Largo" "Larghetto"   "Adagio"      "Andante"    
 [8] "Moderato"    "Allegro"     "Vivace"      "Presto"      "Prestissimo"

 [1] "Categories=" "Larghissimo" "Grave"       "Lento/Largo" "Larghetto"   "Adagio"      "Andante"    
 [8] "Moderato"    "Allegro"     "Vivace"      "Presto"      "Prestissimo"

[1] "Cross Table:"
             P
              major minor
  Larghissimo     0     0
  Grave           0     0
  Lento/Largo     9     1
  Larghetto      13    10
  Adagio         63    33
  Andante       551   307
  Moderato      301   121
  Allegro       908   427
  Vivace         75    44
  Presto         86    30
  Prestissimo    14     3
[1] "Distribucions condicionades a columnes:"
       
P       Larghissimo Grave Lento/Largo Larghetto    Adagio   Andante  Moderato   Allegro    Vivace    Presto
  major                     0.9000000 0.5652174 0.6562500 0.6421911 0.7132701 0.6801498 0.6302521 0.7413793
  minor                     0.1000000 0.4347826 0.3437500 0.3578089 0.2867299 0.3198502 0.3697479 0.2586207
       
P       Prestissimo
  major   0.8235294
  minor   0.1764706

[1] "Test Chi quadrat: "

Warning: Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  dades[, k] and as.factor(P)
X-squared = 16.012, df = 8, p-value = 0.04221

[1] "valorsTest:"
$rowpf
       Xquali
P       Larghissimo       Grave Lento/Largo   Larghetto      Adagio     Andante    Moderato     Allegro
  major 0.000000000 0.000000000 0.004455446 0.006435644 0.031188119 0.272772277 0.149009901 0.449504950
  minor 0.000000000 0.000000000 0.001024590 0.010245902 0.033811475 0.314549180 0.123975410 0.437500000
       Xquali
P            Vivace      Presto Prestissimo
  major 0.037128713 0.042574257 0.006930693
  minor 0.045081967 0.030737705 0.003073770

$vtest
       Xquali
P       Larghissimo      Grave Lento/Largo  Larghetto     Adagio    Andante   Moderato    Allegro     Vivace
  major   0.0000000  0.0000000   1.5259103 -1.1198620 -0.3821151 -2.3706107  1.8460768  0.6195929 -1.0446552
  minor   0.0000000  0.0000000  -1.5259103  1.1198620  0.3821151  2.3706107 -1.8460768 -0.6195929  1.0446552
       Xquali
P           Presto Prestissimo
  major  1.5738795   1.3172030
  minor -1.5738795  -1.3172030

$pval
       Xquali
P       Larghissimo       Grave Lento/Largo   Larghetto      Adagio     Andante    Moderato     Allegro
  major 0.500000000 0.500000000 0.063516101 0.131386282 0.351187992 0.008879362 0.032440526 0.267762926
  minor 0.500000000 0.500000000 0.063516101 0.131386282 0.351187992 0.008879362 0.032440526 0.267762926
       Xquali
P            Vivace      Presto Prestissimo
  major 0.148091186 0.057757651 0.093885296
  minor 0.148091186 0.057757651 0.093885296

#descriptors de les classes més significatius. Afegir info qualits
for (c in 1:length(levels(as.factor(P)))) {
  if(!is.na(levels(as.factor(P))[c])){
    print(paste("P.values per class:",levels(as.factor(P))[c]));
    print(sort(pvalk[c,]), digits=3) 
  }
}

[1] "P.values per class: major"
        explicit              key             mode   time_signature      track_genre multiple_artists 
        0.00e+00         0.00e+00         0.00e+00         0.00e+00         0.00e+00         0.00e+00 
       tempo_cat instrumentalness           energy     acousticness      speechiness      duration_ms 
        0.00e+00         3.99e-12         1.12e-06         3.58e-06         6.72e-04         9.37e-04 
         valence            tempo       popularity         liveness     danceability         loudness 
        4.42e-03         4.59e-03         1.76e-02         2.80e-01         4.59e-01         4.85e-01 
[1] "P.values per class: minor"
        explicit              key             mode   time_signature      track_genre multiple_artists 
        0.00e+00         0.00e+00         0.00e+00         0.00e+00         0.00e+00         0.00e+00 
       tempo_cat instrumentalness           energy     acousticness      speechiness      duration_ms 
        0.00e+00         3.99e-12         1.12e-06         3.58e-06         6.72e-04         9.37e-04 
         valence            tempo       popularity         liveness     danceability         loudness 
        4.42e-03         4.59e-03         1.76e-02         2.80e-01         4.59e-01         4.85e-01 

#afegir la informacio de les modalitats de les qualitatives a la llista de pvalues i fer ordenacio global

#saving the dataframe in an external file
#write.table(dd, file = "credscoClean.csv", sep = ";", na = "NA", dec = ".", row.names = FALSE, col.names = TRUE)

Findings:

From the boxplot of valence vs mode, we can see that minor songs tend to have lower valence (sadder mood) than major songs. Similarly, songs in the minor key tend to have lower tempo compared to songs in the major key, although there are outliers.

Interestingly, many songs in the major key are in the key of G, C, D and A. Whereas the most popular key for minor songs are A, B and E.

For popularity, songs in the minor key have a wider range compared to major songs.

Genre vs valence.

library(dplyr)
library(forcats)
plotdata <- dd %>%
  group_by(track_genre) %>%
  summarize(mean_valence = mean(valence))
# plot mean salaries
ggplot(plotdata, 
       aes(x = fct_reorder(track_genre, mean_valence), 
           y = mean_valence)) +
  geom_bar(stat = "identity") +
  scale_x_discrete(guide = guide_axis(angle = 90)) +
  xlab("Genre") + ylab("Mean Valence")

One genre stood out when looking at highest valence: r&b. The Sleep genre has the lowest mean valence.

Next we can examine the relationship between genre and energy, by calculating the mean.

plotdata2 <- dd %>%
  group_by(track_genre) %>%
  summarize(mean_energy = mean(energy))
# plot mean salaries
ggplot(plotdata2, 
       aes(x = fct_reorder(track_genre, mean_energy), 
           y = mean_energy)) +
  geom_bar(stat = "identity") +
  scale_x_discrete(guide = guide_axis(angle = 90))

Classical songs have the least mean energy, and drum-and-bass songs have the highest mean energy.

We can also examine the relationship between genre and danceability

plotdata3 <- dd %>%
  group_by(track_genre) %>%
  summarize(mean_danceability = mean(danceability))
# plot mean salaries
ggplot(plotdata3, 
       aes(x = fct_reorder(track_genre, mean_danceability), 
           y = mean_danceability)) +
  geom_bar(stat = "identity") +
  scale_x_discrete(guide = guide_axis(angle = 90))

plotdata2 <- dd %>%
  group_by(track_genre) %>%
  summarize(mean_energy = mean(energy))
# plot mean salaries
ggplot(plotdata2, 
       aes(x = fct_reorder(track_genre, mean_energy), 
           y = mean_energy)) +
  geom_bar(stat = "identity") +
  scale_x_discrete(guide = guide_axis(angle = 90))

ggplot(dd, 
       aes(x = mode, 
           y = valence)) +
  geom_boxplot() +
  labs(title = "Valence distribution by mode")

Energy vs mode

ggplot(dd, 
       aes(x = mode, 
           y = energy)) +
  geom_boxplot() +
  labs(title = "Energy distribution by mode")

Minor songs actually have a higher range of energy compared to major songs, which is interesting because one would think that there would be more happy songs (typically major key) with higher energy. But this could be because many of the latin songs are in minor key.

ggplot(dd, 
       aes(x = mode, 
           y = acousticness)) +
  geom_boxplot() +
  labs(title = "Acousticness distribution by mode")

Looking at valence vs explicitness, we see that songs that are explicit tend to have lower valence than songs that are clean.

ggplot(dd, 
       aes(x = explicit, 
           y = valence)) +
  geom_boxplot() +
  labs(title = "Valence distribution by explicit") +
    scale_x_discrete(guide = guide_axis(angle = 90))

Looking at valence vs explicitness, we see that songs that are explicit tend to have lower valence than songs that are clean.

ggplot(dd, 
       aes(x = explicit, 
           y = valence)) +
  geom_boxplot() +
  labs(title = "Valence distribution by explicit") +
    scale_x_discrete(guide = guide_axis(angle = 90))

Numerical vs Numerical

numerical_only <- dd %>% select(1:2, 4:5, 7, 9:14)
pairs(numerical_only)

panel.cor <- function(x, y, digits = 2, prefix = "", cex.cor, ...)
{
    usr <- par("usr"); on.exit(par(usr))
    par(usr = c(0, 1, 0, 1))
    r <- abs(cor(x, y))
    txt <- format(c(r, 0.123456789), digits = digits)[1]
    txt <- paste0(prefix, txt)
    if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
    text(0.5, 0.5, txt, cex = cex.cor * r)
}
pairs(numerical_only, lower.panel = panel.smooth, upper.panel = panel.cor,
      gap=0, row1attop=FALSE)